User-driven narrative variation in large story domains using Monte Carlo Tree Search

Bilal Kartal, John Koenig, Stephen J. Guy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Planning-based techniques are powerful tools for automated narrative generation, however, as the planning domain grows in the number of possible actions traditional planning techniques suffer from a combinatorial explosion. In this work, we apply Monte Carlo Tree Search to goal-driven narrative generation. We demonstrate our approach to have an order of magnitude improvement in performance over traditional search techniques when planning over large story domains. Additionally, we propose a Bayesian story evaluation method to guide the planning towards believable narratives which achieve user-defined goals. Finally, we present an interactive user interface which enables users of our framework to modify the believability of different actions, resulting in greater narrative variety.

Original languageEnglish (US)
Title of host publication13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages69-76
Number of pages8
ISBN (Electronic)9781634391313
StatePublished - Jan 1 2014
Event13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France
Duration: May 5 2014May 9 2014

Publication series

Name13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Volume1

Other

Other13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Country/TerritoryFrance
CityParis
Period5/5/145/9/14

Keywords

  • Exploration versus exploitation
  • MCTS
  • Monte Carlo Tree Search
  • UCB
  • Upper confidence bounds

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